SlowGuess/NAACL_2021_OCR huggingface dataset:
Haoyu Zhang, Jingjing Cai, Jianjun Xu, and Ji Wang. 2019a. Pretraining-based natural language generation for text summarization. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 789-797, Hong Kong, China. Association for Computational Linguistics.
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019b. Bertscore: Evaluating text generation with bert. In International Conference on Learning Representations.
Ming Zhong, Pengfei Liu, Danqing Wang, Xipeng Qiu, and Xuanjing Huang. 2019. Searching for effective neural extractive summarization: What works and what's next. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1049-1058, Florence, Italy. Association for Computational Linguistics.
Deyu Zhou, Linsen Guo, and Yulan He. 2018. Neural storyline extraction model for storyline generation from news articles. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1727-1736, New Orleans, Louisiana. Association for Computational Linguistics.
Hao Zhou, Yue Zhang, Shujian Huang, Junsheng Zhou, Xin-Yu Dai, and Jiajun Chen. 2016. A search-based dynamic reranking model for dependency parsing. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1393-1402, Berlin, Germany. Association for Computational Linguistics.
Long Zhou, Wenpeng Hu, Jiajun Zhang, and Chengqing Zong. 2017. Neural system combination for machine translation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 378-384, Vancouver, Canada. Association for Computational Linguistics.
A Features for RankSVM
We use 18 features as defined below for RankSVM:
1. document length.
2. candidate summary length.
3. rouge-1, rouge-2, rouge-L between source documents and candidates summaries.
4. copy length: the length of summary's fragments appeared in the source document.
5. fragment coverage, fragment density, compression ratio as defined in Grusky et al. (2018).
6. novelty: the ratio of novel  $k$ -grams ( $k \in \{1, 2, 3, 4\}$ ) in the candidate summaries.
7. repetition: the ratio of repeated  $k$ -grams ( $k \in \{1, 2, 3, 4\}$ ) in the candidate summaries.
8. sentence fusion ratio: the ratio of sentences in the candidate summaries that combine the content of two source document sentences.